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Record W4244889685 · doi:10.4161/cbt.5.4.2672

Association of a Kaposi's Sarcoma Herpes Virus gene with B cell Lymphoma Provides Possible Clues for Therapy

2006· article· en· W4244889685 on OpenAlexaboutno aff

Bibliographic record

VenueCancer Biology & Therapy · 2006
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsLymphomaCancerTranslational researchVirusImmunologyMedicineCancer researchOncologyBiologyVirologyInternal medicinePathology

Abstract

fetched live from OpenAlex

AbstractResearch showing how Kaposi sarcoma-associated herpes virus (KSHV) induced a pre-lymphoma condition, and in some cases true lymphoma in mice, represents a major breakthrough in understanding the genesis of this particular type of blood cancer.The findings are featured as the cover story in march, 2006 in the Journal of Clinical Investigation. The research was led by Dirk Dittmer, Ph.D., University of North Carolina at Chapel Hill, Lineberger Comprehensive Cancer Center, and funded by The Leukemia & Lymphoma Society.Dr. Dittmer's work shows that a particular viral gene in the KSHV caused the pre-malignant and malignant changes, further suggesting that this gene might be a novel disease marker and a valid target for anti-lymphoma therapy. Because only the cancer cells carry the virus, one or more of the existing anti-viral therapies might help patients with this particular type of B cell lymphoma, and specific gene tests might be used to predict if patients with virally-associated lymphomas will benefit from treatment with anti-viral drugs.Dr. Dittmer is a recipient of a Translational Research Grant from the Society, a program that supports outstanding investigative research showing strong promise of translating basic biomedical knowledge into new and better treatments for blood cancers. The program's goal is to accelerate the transfer of findings from the laboratory to clinical application, ultimately prolonging and enhancing patients' lives."The goal of the Translational Research Program is to provide researchers with the resources to advance diagnosis, prevention or treatment of blood cancers in the near term," said Marshall Lichtman, M.D., executive vice president, Research & Medical Programs. "Dr. Dittmer's research may lead to earlier diagnosis, potentially prevention and provide the insights to develop a better treatment for a particular group of lymphoma patients."Dr. Dittmer said that the Society's support was fundamental in moving his research forward."I am truly grateful to The Leukemia & Lymphoma Society for having confidence in my work and providing me with this funding," said Dr. Dittmer. "I am hopeful that our work studying how herpes viruses alter normal immune cells in biology to create malignancies will help determine whether certain lymphoma patients might respond to anti-viral therapy."About The Leukemia & Lymphoma SocietyThe Leukemia & Lymphoma Society, headquartered in White Plains, NY, with 66 chapters in the United States and Canada, is the world's largest voluntary health organization dedicated to funding blood cancer research and providing education and patient services. The Society's mission: Cure leukemia, lymphoma, Hodgkin's disease and myeloma, and improve the quality of life of patients and their families. Since its founding in 1949, the Society has invested more than $424 million in research specifically targeting leukemia, lymphoma and myeloma. Last year alone, the Society made 2.5 million contacts with patients, caregivers and healthcare professionals.For more information about blood cancer, visit www.LLS.org or call the Society's Information Resource Center (IRC), a call center staffed by master's level social workers, nurses and health educators who provide information, support and resources to patients and their families and caregivers. IRC information specialists are available at (800) 955-4572, Monday through Friday, 9 a.m. to 6 p.m. ET.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.276
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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